Research Article
A Vision Transformer Approach to Fundus Image Classification
@INPROCEEDINGS{10.1007/978-3-031-60665-6_11, author={Danilo Leite and Jos\^{e} Camara and Jo\"{a}o Rodrigues and Ant\^{o}nio Cunha}, title={A Vision Transformer Approach to Fundus Image Classification}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Fundus Image Vision transformers BRSET}, doi={10.1007/978-3-031-60665-6_11} }- Danilo Leite
José Camara
João Rodrigues
António Cunha
Year: 2024
A Vision Transformer Approach to Fundus Image Classification
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_11
Abstract
Glaucoma is a condition that affects the optic nerve, with loss of retinal nerve fibers, increased excavation of the optic nerve, and a progressive decrease in the visual field. It is the leading cause of irreversible blindness in the world. Manual classification of glaucoma is a complex and time-consuming process that requires assessing a variety of ocular features by experienced clinicians. Automated detection can assist the specialist in early diagnosis and effective treatment of glaucoma and prevent vision loss. This study developed a deep learning model based on vision transformers, called ViT-BRSET, to detect patients with increased excavation of the optic nerve automatically. ViT-BRSET is a neural network architecture that is particularly effective for computer vision tasks. The results of this study were promising, with an accuracy of 0.94, an F1-score of 0.91, and a recall of 0.94. The model was trained on a new dataset called BRSET, which consists of 16,112 fundus images of patients with increased excavation of the optic nerve. The results of this study suggest that ViT-BRSET has the potential to improve early diagnosis through early detection of optic nerve excavation, one of the main signs of glaucomatous disease. ViT-BRSET can be used to mass-screen patients, identifying those who need further examination by a doctor.
